This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting...

This study explores the volatility spillover effects between clean and dirty cryptocurrencies and key financial indices, specifically focusing on Green Finance Indices (such as solar, wind, and nuclear) and Economic Indices (like the Baltic Dry Index and CRB Index). Employing the diagonal BEKK...

This paper explores the volatility forecasting implications of a model in which the friction in high-frequency prices is related to the true underlying volatility. The contribution of this paper is to propose a framework under which the realized variance may improve volatility forecasting if the...